Method and device for estimating positioning accuracy, method and device for training accuracy estimation model, electronic device, and computer program

Abstract

The embodiments of the present invention provide a method, device, electronic device, and storage medium for estimating positioning accuracy, and are applied to fields such as maps, navigation, autonomous driving, V2X (Vehicle-to-everything), ITS, and cloud computing. The method for estimating positioning accuracy includes the steps of acquiring first driving information of a vehicle from a sensor, acquiring first position information of the vehicle and a first intermediate variable used in a process of determining the first position information based on the first driving information, determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variable, and inputting the first position information and the first intermediate variable into the target accuracy estimation model to acquire second position information of the vehicle and an accuracy error of the first position information relative to the second position information. The embodiments of the present invention can effectively evaluate the accuracy error of high-precision positioning.

Description

[Technical Field]

[0001] This application claims priority from a Chinese patent application filed on July 15, 2022, bearing application number 202210837512.8 and entitled "Method, apparatus, electronic device and storage medium for estimating positioning accuracy," the entire contents of which are incorporated herein by reference.

[0002] The present invention relates to the field of Intelligent Transport Systems (ITS), and more particularly to a method, device, electronic device and storage medium for estimating positioning accuracy. [Background technology]

[0003] Intelligence is one of the main trends in the development of automobiles today. The process of automobile intelligence depends on the maturity of vehicle sensors, algorithm software, and decision-making platforms. The combination of high-precision positioning and high-precision maps provides vehicles with accurate absolute position information, which, combined with relative position information from sensors, improves the safety of smart driving. As automobile intelligence advances, the importance of high-precision positioning becomes increasingly prominent.

[0004] Accuracy error assessment is an important part of high-precision positioning. The accuracy error of a single positioning, i.e., the magnitude of the error that may be contained in the current positioning, is very useful for subsequent vehicle control, collision avoidance, smart vehicle speed control, route planning, and behavioral decision-making. However, currently, there is no method to effectively assess the accuracy error of high-precision positioning. Summary of the Invention

[0005] The embodiments of the present invention provide a method, device, electronic device, and storage medium for estimating positioning accuracy, which are capable of effectively evaluating the accuracy error of high-precision positioning.

[0006] In a first aspect of an embodiment of the present invention, a method for estimating positioning accuracy is provided, comprising the steps of: acquiring first driving information of a vehicle from a sensor; acquiring first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information; determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, wherein the accuracy estimation model is obtained by training a machine learning model based on a training sample set, and training samples in the training sample set include position information of a sample vehicle and intermediate variables used in the process of determining the position information; and inputting the first position information and the first intermediate variables into the target accuracy estimation model, and acquiring second position information of the vehicle and an accuracy error of the first position information relative to the second position information.

[0007] A second aspect of an embodiment of the present invention provides a method for training an accuracy estimation model, comprising the steps of: acquiring second driving information of a sample vehicle at a first time from a sensor; acquiring third position information of the sample vehicle and second intermediate variables used in the process of determining the third position information based on the second driving information; acquiring fourth position information of the sample vehicle at the first time from a positioning device; determining a training sample set including the third position information, the fourth position information, and the second intermediate variables; and training the accuracy estimation model based on the training sample set.

[0008] A third aspect of the present invention provides a positioning accuracy estimation device including an acquisition unit that acquires first driving information of a vehicle from a sensor; a processing unit that acquires first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information; and a determination unit that determines a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, wherein the accuracy estimation model is obtained by training a machine learning model based on a training sample set, and training samples in the training sample set include position information of a sample vehicle and intermediate variables used in the process of determining the position information, and the target accuracy estimation model receives the first position information and the first intermediate variables as input, and acquires second position information of the vehicle and an accuracy error of the first position information relative to the second position information.

[0009] In a fourth aspect of an embodiment of the present invention, there is provided a training device for an accuracy estimation model, the device including: a first acquisition unit that acquires second driving information of a sample vehicle at a first time from a sensor; a processing unit that acquires third position information of the sample vehicle and second intermediate variables used in the process of determining the third position information based on the second driving information; a second acquisition unit that acquires fourth position information of the sample vehicle at the first time from a positioning device; a determination unit that determines a training sample set including the third position information, the fourth position information, and the second intermediate variables; and a training unit that trains the accuracy estimation model based on the training sample set.

[0010] In a fifth aspect of an embodiment of the present invention, there is provided an electronic device including a processor capable of executing computer instructions and a memory having computer instructions stored therein, the computer instructions being configured to be loaded by the processor to perform a method according to the first or second aspect above.

[0011] In a sixth aspect of an embodiment of the present invention, there is provided a computer readable storage medium having stored thereon computer instructions which, when loaded and executed by a processor of a computing device, cause the computing device to perform a method according to the first or second aspect above.

[0012] In a seventh aspect of an embodiment of the present invention, there is provided a computer program product or a computer program comprising computer instructions, the computer program product or the computer program being configured to read and execute the computer instructions from a computer readable storage medium by a processor of a computing device to cause the computing device to perform a method according to the first or second aspect above.

[0013] According to the above technical means, an embodiment of the present invention can effectively evaluate the accuracy error of high-precision positioning by obtaining first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information, determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, inputting the first position information and the first intermediate variables into the target accuracy estimation model, and obtaining second position information of the vehicle and the accuracy error of the first position information relative to the second position information. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a schematic diagram of the architecture of a system according to an embodiment of the present invention; [Figure 2] 1 is a schematic flowchart of a method for training a model according to an embodiment of the present invention; [Figure 3] 1 is a schematic diagram of a network architecture according to an embodiment of the present invention; [Figure 4] 1 is a schematic flowchart of a method for obtaining training sample data according to an embodiment of the present invention; [Figure 5]1 is a schematic flowchart of a method for estimating positioning accuracy according to an embodiment of the present invention. [Figure 6] 1 is a diagram illustrating a specific example of a method for estimating positioning accuracy according to an embodiment of the present invention. [Figure 7] 1 is a schematic block diagram of a positioning accuracy estimation device according to an embodiment of the present invention; [Figure 8] 1 is a schematic block diagram of a model training device according to an embodiment of the present invention; [Figure 9] 1 is a schematic block diagram of an electronic device according to an embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0015] The following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. It is clear that the described embodiments are only a part of the embodiments of the present invention, and are not all of the embodiments. All other embodiments that can be obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of the present invention.

[0016] In the embodiments of the present invention, "B corresponding to A" means that B is related to A. In one aspect, B may be determined based on A. Determining B based on A does not mean determining B simply based on A, but B may be determined based on A and / or other information.

[0017] In this description, unless otherwise specified, "at least one" means one or more, and "plurality" means two or more. Furthermore, "and / or" describes a relationship between related objects and means three possible relationships. For example, A and / or B may mean that only A is present, that both A and B are present, or that only B is present, where A and B may be singular or plural. The symbol " / " generally indicates an "or" relationship between the related objects before and after it. "At least one of" or similar expressions refers to any combination of these items, including any combination of singular (individual) or plural (multiple). For example, "at least one of a, b, or c" may mean a, b, c, ab, ac, bc, or abc, where a, b, and c may be singular or plural.

[0018] In addition, the descriptions such as "first" and "second" in the embodiments of the present invention are merely used to distinguish the objects of explanation, and do not distinguish between sequential orders. They do not indicate any particular limitations on the number of devices in the embodiments of the present invention, and do not limit the embodiments of the present invention.

[0019] It should be noted that any particular feature, structure, or characteristic associated with any embodiment herein is included in at least one embodiment of the present invention, and that any particular feature, structure, or characteristic described herein may be combined in any suitable manner in one or more embodiments.

[0020] Additionally, the terms "comprises" and "having," and any variations thereof, are intended to cover non-exclusive inclusions; for example, a process, method, system, product, or server comprising a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include those not explicitly listed or other steps or units inherent to the process, method, product, or apparatus.

[0021] Embodiments of the present invention are applied in the field of artificial intelligence.

[0022] Here, artificial intelligence (AI) refers to theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, evolve, and extend human intelligence, sense the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology in computer science that seeks to understand the nature of intelligence and create new intelligent machines that respond in a manner similar to human intelligence. AI involves researching the design principles and implementation methods of various intelligent machines, and endowing them with the capabilities of sensing, reasoning, and decision-making.

[0023] Artificial intelligence technology is a comprehensive field that covers a wide range of fields, including both hardware and software. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operation / interaction systems, and mechatronics. Software technologies for AI mainly include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0024] With the research and advancement of artificial intelligence technology, it is being researched and applied in many fields, such as smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, autonomous driving, drones, robots, smart medical care, smart customer service, etc. With the development of technology, it is expected that artificial intelligence technology will be applied in more and more fields and will play an increasingly important role.

[0025] An embodiment of the present invention relates to an autonomous driving technology in artificial intelligence technology. The autonomous driving technology is a technology in which a computer can automatically and safely operate a vehicle without any human intervention through the cooperation of artificial intelligence, computer vision, radar, monitoring devices, and a global positioning system. The autonomous driving technology typically includes technologies such as high-precision maps, environment sensing, behavior decision-making, route planning, and motion control. The autonomous driving technology has a wide range of applications. Specifically, a technical solution according to an embodiment of the present invention relates to a technology for evaluating positioning accuracy errors, and can be applied to evaluating the positioning accuracy of autonomous driving.

[0026] An embodiment of the present invention may also relate to machine learning (ML) in artificial intelligence technology. ML is a multidisciplinary field that encompasses many disciplines, including probability theory, statistics, approximation theory, convex analysis, and algorithmic complexity theory. It focuses on how computers simulate or realize human learning behavior, acquire new knowledge or skills, and reorganize existing knowledge structures to continually improve their performance. Machine learning is at the heart of artificial intelligence and is a fundamental method for endowing computers with intelligence, with applications spanning various fields of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, trust networks, reinforcement learning, transition learning, inductive learning, and supervised learning.

[0027] 1 is a schematic diagram of a system architecture according to an embodiment of the present invention. As shown in FIG. 1, the system architecture may include a user device 101, a data collection device 102, a training device 103, an execution device 104, a database 105, and a content library 106.

[0028] Here, the data collection device 102 reads training data from the content library 106 and stores the read training data in the database 105. The training data according to the embodiment of the present invention includes location information #1, location information #2, and intermediate variables used in the process of determining the location information #1. Here, the location information #1 is determined based on the driving information of the sample vehicle acquired by a sensor, and the location information #2 is the location information of the sample vehicle collected by a positioning device.

[0029] The training device 103 trains the machine learning model based on the training data held in the database 105 so that the trained machine learning model can effectively evaluate the positioning accuracy. The machine learning model obtained by the training device 103 can be applied to different systems or devices.

[0030] 1, the execution device 104 also includes an I / O interface 107 for data interaction with external devices. For example, vehicle driving information collected by a sensor and transmitted by the user device 101 is received via the I / O interface. The calculation module 109 in the execution device 104 obtains the vehicle positioning accuracy based on the driving information using a trained machine learning model. The machine learning model may transmit corresponding results to the user device 101 via the I / O interface.

[0031] Here, the user device 101 may include a smart car, an in-vehicle terminal, a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a mobile internet device (MID), or other terminal devices.

[0032] The execution device 104 may be a server.

[0033] As an example, the server may be a computing device such as a rack server, a blade server, a tower server, or a cabinet server, etc. The server may be a standalone test server or a test server cluster consisting of multiple test servers.

[0034] The server may be one or more servers, where there are at least two servers for providing different services and / or at least two servers for providing the same service, e.g., in a load-balancing manner, but embodiments of the present invention are not limited thereto.

[0035] Here, the server may be an independent physical server, a server cluster or a distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDNs (Content Delivery Networks), and big data and artificial intelligence platforms. The server may also be a node of a blockchain.

[0036] In this embodiment, the execution device 104 is connected to the user device 101 via a network, which may be a wireless or wired network such as a corporate intranet, the Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth, Wi-Fi, a telephone network, etc.

[0037] 1 is merely a schematic diagram of the architecture of a system according to an embodiment of the present invention, and is not limited to the positional relationships between the illustrated apparatuses, devices, modules, etc. In some aspects, the data collection apparatus 102 may be the same apparatus as the user apparatus 101, the training apparatus 103, and the execution apparatus 104. The database 105 may be distributed on one server or across multiple servers, and the content library 106 may be distributed on one server or across multiple servers.

[0038] The following describes terms related to embodiments of the present invention.

[0039] High-precision positioning: Usually refers to positioning with an accuracy of decimeters or centimeters or higher, which can provide vehicles with relatively high-precision positioning results and is one of the essential core technologies for safe driving such as autonomous driving and remote driving. High-precision positioning plays an important role in accurate lateral and longitudinal positioning of vehicles, obstacle detection and collision avoidance, smart vehicle speed control, route planning and behavior decision-making, etc.

[0040] High-precision positioning allows a vehicle to accurately determine its own absolute position using a high-precision absolute reference system. The high-precision absolute reference system may be, for example, a high-precision map. The map layer of the high-precision map contains numerous road attribute elements with centimeter-level accuracy, including, but not limited to, information on road edges, lane edges, centerlines, etc. The vehicle can perform accurate navigation while traveling based on the information in the high-precision map.

[0041] Multi-source fusion positioning: This is a technology that combines multiple positioning technologies based on information fusion policies, and can combine related positioning methods such as satellite positioning, wireless communication signal positioning, and sensor positioning. This can achieve better fusion positioning results than a single positioning method. Multi-source fusion positioning can achieve high-precision positioning.

[0042] Here, satellite positioning is a technology that performs positioning using satellites (e.g., GNSS). Wireless communication signal positioning is a technology that performs positioning using wireless communication signals (e.g., WiFi signals, Ultra Wideband (UWB) signals). Sensor positioning is a technology that performs positioning using information collected by sensors (e.g., visual sensors, vehicle speed sensors, Inertial Measurement Unit (IMU) sensors).

[0043] Visual positioning: A technology for positioning using information collected by visual sensors. Visual positioning may use visual sensors to recognize road attribute elements in the high-precision map positioning layer and estimate vehicle position information using visual algorithms. Visual positioning offers significant cost advantages because it allows for a high degree of reuse of high-precision maps and sensors such as cameras, and does not require additional hardware.

[0044] Inertial positioning: A positioning technology that uses information collected by an IMU sensor. Inertial positioning uses an IMU sensor to measure the angular velocity and acceleration of a vehicle, and automatically estimates the instantaneous velocity and position of the carrier using Newton's laws of motion. It is not dependent on external information, does not emit energy to the outside world, is not interfered with, and has good concealment properties.

[0045] Global Navigation Satellite System (GNSS): Generally refers to a satellite navigation system, including global, regional, and augmented satellite navigation systems, such as the Global Positioning System (GPS), Glonass, the European Galileo, and the Beidou satellite navigation system, as well as related augmentation systems, such as the Wide Area Augmentation System (WAAS), the European Geostationary Navigation System (EGNOS), and the Multi-Function Transport Satellite Augmentation System (MSAS), and may include other satellite navigation systems under construction or to be constructed in the future. The international GNSS system is a complex, multi-system, multi-level, and multi-mode combination system.

[0046] Typically, conventional satellite positioning achieves positioning by simultaneously receiving signals from multiple satellites. However, certain fluctuations occur when satellite signals pass through the ionosphere and troposphere, resulting in errors and limiting the accuracy of positioning to the meter level. Augmented terrestrial base stations can improve positioning accuracy by calculating satellite positioning errors and further correcting the position using real-time kinematic (RTK) carrier phase differential technology. For example, satellite positioning accuracy can be increased from the meter level to the centimeter level.

[0047] Additionally, satellite positioning is prone to signal weakness or loss when there are obstructions on the ground, in which case the vehicle may utilize inertial or visual positioning, or other positioning capabilities, to ensure the navigation system continues to operate.

[0048] RTK carrier phase differential technology: A differential method that processes carrier phase measurements from two measurement stations in real time, and calculates coordinates by transmitting the carrier phase collected by the reference station to the user receiver to determine the difference. RTK carrier phase differential technology uses a dynamic real-time carrier phase differential method to achieve centimeter-level positioning accuracy in real time in the field, providing new measurement principles and methods for construction sampling, topographical measurement, and various control measurements, improving work efficiency.

[0049] Accuracy error assessment is an important part of high-precision positioning. The accuracy error of a single positioning, i.e., the magnitude of the error that may be contained in the current positioning, is very useful for subsequent vehicle control, collision avoidance, smart vehicle speed control, route planning, and behavioral decision-making. However, currently, there is no method to effectively assess the accuracy error of high-precision positioning.

[0050] In view of the above problems, embodiments of the present invention provide a method, device, electronic device, and storage medium for estimating positioning accuracy that can effectively evaluate the accuracy error of high-precision positioning.

[0051] Specifically, an embodiment of the present invention may acquire first driving information of a vehicle from a sensor, acquire first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information, determine a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, input the first position information and the first intermediate variables into the target accuracy estimation model, and acquire second position information of the vehicle and an accuracy error of the first position information relative to the second position information.

[0052] Here, the accuracy estimation model is obtained by training a machine learning model based on a training sample set, and training samples in the training sample set include location information of a sample vehicle and intermediate variables used in the process of determining the location information. As an example, the training sample set may include third location information, fourth location information, and a second intermediate variable used in the process of determining the third location information. The third location information is determined based on second driving information of the sample vehicle collected by a sensor at a first time, and the fourth location information includes location information of the sample vehicle collected by a positioning device at the first time.

[0053] Therefore, an embodiment of the present invention can effectively evaluate the accuracy error of high-precision positioning by obtaining first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information, determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, inputting the first position information and the first intermediate variables into the target accuracy estimation model, and obtaining second position information of the vehicle and the accuracy error of the first position information relative to the second position information.

[0054] In an embodiment of the present invention, the accuracy estimation model can realize prediction of the actual position information of the vehicle collected by the positioning equipment and estimation of the accuracy error of the position information for the actual position information based on the vehicle's positioning information determined based on the vehicle's driving information collected by the sensor and intermediate variables used in the process of determining the position information.

[0055] The method for estimating positioning accuracy according to an embodiment of the present invention may be divided into two stages: an offline model construction stage and an online estimation stage. The offline model construction stage may be trained to obtain an accuracy estimation model based on collected sample vehicle data. The online estimation stage may collect vehicle driving information in real time and estimate the vehicle positioning accuracy based on the driving information and the accuracy estimation model constructed in the offline model construction stage.

[0056] Each step will be described in detail below with reference to the drawings.

[0057] First, the offline model construction stage will be described.

[0058] 2 is a schematic flowchart of a method 200 for training a model according to an embodiment of the present invention. The method 200 may be performed by any electronic device having data processing capabilities. For example, the electronic device may be implemented as a server or a terminal device, or may be implemented as the training device 103 in FIG. 1, but the present invention is not limited thereto.

[0059] 3 is a schematic diagram of a network architecture according to an embodiment of the present invention. The network architecture includes a vehicle information collection module 301, a real-time positioning module 302, an information statistics module 303, a true value collection module 304, a model construction module 305, an information judgment module 306, and an accuracy estimation model 307. Here, the vehicle information collection module 301, the real-time positioning module 302, the information statistics module 303, the true value collection module 304, and the model construction module 305 may be used for model training in the offline model construction phase. The model training method 200 will now be described with reference to FIG. 3.

[0060] As shown in FIG. 2, the method 200 includes steps 210-250.

[0061] Step 210: Obtain second driving information of the sample vehicle at a first time from the sensor.

[0062] For example, the vehicle information collection module 301 in FIG. 3 may acquire second driving information of the sample vehicle collected by a sensor at a first time. The first time may be, but is not limited to, the current and previous periods. After acquiring the second driving information, the vehicle information collection module 301 may transmit the second driving information to the real-time positioning module 302. Preferably, the second driving information may be transmitted to the information statistics module 303.

[0063] In some aspects, the second driving information includes driving information of the sample vehicle collected by at least one of an inertial measurement unit (IMU) sensor, a vehicle speed sensor, and a visual sensor.

[0064] As an example, the sensor may be various sensors for collecting vehicle driving information installed on the sample vehicle, such as a GNSS sensor, an IMU sensor, a vehicle speed sensor, a visual sensor, or other sensor equipment.

[0065] The driving information of the sample vehicle collected by the GNSS sensor may include, for example, the longitude, latitude, DOP value, accuracy value of the currently positioned sample vehicle, whether the current positioning is valid, whether the current positioning is fixed, etc.

[0066] Here, when there is no obstruction or the obstruction is weak (for example, when driving normally on a road), the GNSS sensor can receive the GNSS signal normally, so the current positioning is valid, and in this case, it may also be said that the GNSS sensor is valid. When there is a relatively large amount of obstruction (for example, in a tunnel, an overpass, or a road area on a high mountain), the GNSS sensor cannot receive the GNSS signal normally, so the current positioning is invalid, and in this case, it may also be said that the GNSS sensor is invalid or expired.

[0067] The solution of the GNSS sensor obtained by using the RTK carrier phase differential technique may be referred to as a fixed solution. When the GNSS signal of the GNSS sensor is blocked (for example, in a high mountain road area), when the RTK carrier phase differential technique cannot be used, other types of solutions, such as floating-point solutions, may be obtained. Whether the current positioning is fixed or not refers to whether the current positioning uses the RTK carrier phase differential technique to obtain a fixed solution. Here, the accuracy of the fixed solution is the highest, reaching the centimeter level.

[0068] The driving information of the sample vehicle collected by the IMU sensor may include, for example, acceleration values ​​in the x-, y-, and z-axis directions of an accelerometer, angular velocities in the x-, y-, and z-axis directions of a gyro, and the like.

[0069] The travel information of the sample vehicle collected by the vehicle speed sensor may include, for example, the speed direction and size of the sample vehicle.

[0070] The driving information of the sample vehicle collected by the visual sensor may include, for example, the equation of the lane in which the sample vehicle is located, the line type of the lane (e.g., solid line, dashed line, white line, or yellow line, etc.), and the coordinates of obstacles.

[0071] Step 220: Obtain third location information of the sample vehicle and second intermediate variables used in the process of determining the third location information based on the second driving information.

[0072] 3 may obtain third location information of the sample vehicle and second intermediate variables used in the process of determining the third location information based on the second driving information. After obtaining the third location information and the second intermediate variables, the real-time positioning module 302 may transmit the third location information and the second intermediate variables to the model building module 305.

[0073] In some aspects, the real-time positioning module 302 may acquire second driving information of the sample vehicle collected by each sensor from the vehicle information collection module 301, and may acquire third position information of the sample vehicle by referring to other information. In some aspects, the other information may include map information such as a high-precision map.

[0074] In some aspects, the third location information may include the longitude, latitude, and heading angle of the sample vehicle.

[0075] Here, the direction angle of the vehicle may be an angle by which the front of the vehicle is offset from a predetermined direction. For example, the direction angle of the vehicle may be an angle by which the front of the vehicle is offset clockwise from due north.

[0076] In the process of determining the third location information of the sample vehicle based on the above second driving information, some intermediate variables, ie, the above second intermediate variables, may be obtained.

[0077] In some aspects, third position information and second intermediate variables may be acquired based on the second travel information and the map information, where the second intermediate variables may include error information between lane information determined based on the second travel information and lane information of the sample vehicle in the map information.

[0078] As an example, the second intermediate variable may include the following two types of information:

[0079] (1) Intermediate variables obtained based on information collected by the visual sensor and the high-precision map, such as the difference between the sensor lane width and the map lane width, the lane optimization distance, the initial optimization error, and the final optimization error. (2) Based on the information collected by the GNSS sensor, the visual sensor, and the intermediate variables obtained from the high-precision map, such as the probability that the positioning point is in each lane, the lane with the highest probability of the positioning point, whether the lane information provided by the GNSS sensor matches the lane information provided by the visual sensor, etc. In some aspects, the second intermediate variables may further include at least one of a covariance of an optimization algorithm used to determine the third position information and a statistical value of the second driving information for the second period of time.

[0080] As an example, the second intermediate variable may further include the following two kinds of information:

[0081] (3) Covariances of the optimization algorithm, such as the covariance of the velocity, the covariance of the IMU sensor deviation, and the covariance of the estimated attitude (e.g., estimated vehicle longitude, latitude, and vehicle heading angle). (4) Statistical values ​​of parameters collected by a statistical sensor within a certain period of time, such as statistical values ​​of information collected by a GNSS sensor within 20 seconds, the average value and variance of vehicle speed within 1 second, the average value and variance of acceleration values ​​in the x, y, and z-axis directions of an accelerometer within 10 seconds, and the average value and variance of angular velocity values ​​in the x, y, and z-axis directions of a gyroscope, the distance between the position of the last positioning point obtained by a GNSS sensor and the positioning result, etc. Here, in (1) above, the lane optimization distance may refer to the distance obtained by optimizing the lane obtained by visual positioning when the lane marking of the lane where the vehicle is located, obtained by visual positioning, is aligned with the lane marking of the lane where the vehicle is located on the high-precision map. The initial optimization error may refer to the error between the lane marking of the lane where the vehicle is located, obtained by visual positioning, and the lane marking of the lane where the vehicle is located on the high-precision map before they are aligned. The final optimization error may refer to the error between the lane marking of the lane where the vehicle is located, obtained by visual positioning, and the lane marking of the lane where the vehicle is located on the high-precision map after they are aligned.

[0082] In the above (2), the GNSS sensor may perform high-precision positioning using RTK carrier phase differential technology, obtain the probability that the vehicle's positioning point is located in each lane, and the lane with the highest probability of the positioning point may be determined as the lane in which the vehicle is located.The vision sensor may further obtain the probability that the vehicle's positioning point is located in each lane based on the optimization result in the above (1), and determine the lane with the highest probability of the positioning point as the lane in which the vehicle is located.

[0083] In the above (3), different optimization algorithms and different optimization goals may be adopted, and the present invention is not limited thereto. The optimization algorithm may be, for example, an optimization algorithm based on a global optimization format that optimizes information collected by all sensors within a certain period of time, or an optimization algorithm based on a filtering format that optimizes using information collected by sensors at previous and later times.

[0084] In (4) above, the statistical values ​​of the information collected by the GNSS sensor within 20 seconds include, but are not limited to, the valid rate, the fixed rate, the average value and variance of the accuracy value, the average value and variance of the DOP value, the accuracy of the last GNSS positioning point, the DOP value, whether it is fixed or not, whether it is valid or not, etc.

[0085] Here, the valid proportion may refer to the proportion of valid positioning of the GNSS sensor to all positioning of the GNSS sensor, and the fixed proportion may refer to the proportion of fixed solutions to all positioning results obtained by the GNSS sensor.

[0086] 3 may tally the statistical values ​​of the parameters provided by the vehicle information collection module 301 within a certain period of time to obtain the statistical value (4), but the present invention is not limited to this. After obtaining the statistical values, the information statistics module 303 may transmit the statistical values ​​to the real-time positioning module 302 or to the model construction module 305.

[0087] Step 230: Obtain fourth position information of the sample vehicle collected at the first time by the positioning device.

[0088] For example, the positioning device may be the true value collection module 304 in Fig. 3 and may be installed in the sample vehicle. Therefore, the fourth position information may be referred to as true value position information, that is, the fourth position information may be regarded as the actual position of the sample vehicle, but the present invention is not limited thereto.

[0089] The positioning equipment may have high positioning accuracy, for example, equipment such as SPAN-ISA-100C or SPAN-CPT. The higher the accuracy of the positioning equipment used, the more effective the estimation of positioning accuracy.

[0090] In some aspects, the fourth location information may include the longitude, latitude, and heading angle of the sample vehicle.

[0091] Step 240: Determine a training sample set including the third position information, the fourth position information, and the second intermediate variable.

[0092] 3 may determine a training sample set. The training sample set may include a number of training samples, and each training sample may include the third position information obtained in step 220 above, the second intermediate variable, and the fourth position information in step 230.

[0093] Since different vehicles have different Operational Design Domains (ODDs), for example, an autonomous vehicle may have a control system designed for driving in an urban environment and a control system designed for driving on a highway, so a large amount of collected data is needed in different scenarios, such as to cover at least highways, urban highways, urban general roads, highway or urban tunnel sections, etc.

[0094] An embodiment of the present invention may generate a training sample set by collecting data generated by a large number of sample vehicles while driving in different scenarios. For example, sensors may be used to collect driving information while the sample vehicles are driving, and positioning devices may be used to collect location information while the sample vehicles are driving, on a sufficiently large number of sections such as expressways, urban expressways, and urban general roads. For example, data may be collected for a distance of at least 5,000 kilometers, or 10,000 kilometers or more in each scenario.

[0095] In one preferred embodiment, when determining the training sample set, the positioning result (e.g., the third location information) obtained by the real-time positioning module 302 and the positioning result (e.g., the fourth location information) obtained by the true value collection module 304 may be time-aligned, and all intermediate variables of the real-time positioning module 302 may be extracted. As an example, one training sample may be generated at a certain time point. That is, each training sample may include the positioning result obtained by the real-time positioning module 302, the positioning result obtained by the true value collection module 304, and the intermediate variables of the real-time positioning module 302 at a corresponding time point.

[0096] In some embodiments, for a tunnel section, a simulation of the tunnel section may be performed on data of a sample vehicle collected on a normal section (a section that does not include a tunnel) to obtain training sample data corresponding to the tunnel section.

[0097] Specifically, in tunnel sections, GNSS signals are blocked, so data collected by GNSS sensors cannot be obtained. If one relies solely on other sensors, such as IMU sensors, estimating the accumulated errors generated cannot meet the accuracy requirements, and the accuracy of the location information collected by positioning equipment in tunnel sections may be reduced.

[0098] Typically, autonomous driving or remote driving technology requires that a relatively high level of positioning accuracy be maintained for a certain period of time or distance after entering a tunnel. Therefore, a tunnel section may be simulated by artificially invalidating a portion of a file collected by a GNSS sensor. Accordingly, the section corresponding to the invalidated portion of the file collected by the GNSS sensor may be considered to be a tunnel section. This allows training sample data corresponding to the tunnel section to be obtained, and the fourth position information of the sample vehicle collected by the positioning device in the training sample has sufficiently high accuracy compared to the position information collected by the positioning device in an actual tunnel.

[0099] 4 is a schematic flowchart of a method 400 for acquiring training sample data according to an embodiment of the present invention. In the method 400, the driving information of a sample vehicle collected by a GNSS sensor may be periodically disabled and enabled in a time sequence to generate training sample data corresponding to a tunnel section.

[0100] It should be noted that while Figure 4 illustrates steps or operations of a method for obtaining a training sample set corresponding to a tunnel section, these steps or operations are merely exemplary and embodiments of the present invention may perform other operations or variations of the operations in the figure. Furthermore, the steps in Figure 4 may be performed in an order different from that shown in the figure, and not all of the operations in the figure may be performed.

[0101] As shown in FIG. 4, the method 400 includes steps 401-409.

[0102] Step 401: Obtain all valid files collected by the GNSS sensor.

[0103] For example, in the road measurement stage, travel information of the sample vehicle collected by a sensor in a road section in a scenario without a tunnel may be acquired, and the travel information of the vehicle collected by the GNSS sensor may be saved in a file format.

[0104] Step 402: It is determined whether a time longer than 200 seconds has elapsed since the start time of the file.

[0105] Here, the file start time may be the time when the sample vehicle started traveling, i.e., the time when the GNSS sensor started collecting vehicle traveling information. By determining whether a time longer than 200 seconds has elapsed since the file start time, the acquired training sample data for tunnel simulation can include the sample vehicle's traveling information collected by the GNSS sensor 200 seconds before entering the tunnel.

[0106] Step 403: Determine whether the speed has ever exceeded 20 km / h.

[0107] Here, by determining whether the speed has ever exceeded 20 km / h, it is possible to ensure that the vehicle is in a normal driving state, thereby ensuring that the vehicle driving information obtained by the sensor is useful.

[0108] Step 404: The file collected by the GNSS sensor is set to invalid.

[0109] Preferably, when invalidating a file collected by a GNSS sensor, information in a file formed from vehicle driving information collected by sensors other than the GNSS sensor of the sample vehicle (e.g., an IMU sensor, a visual sensor, a speed sensor, etc.) may be read, and the location information of the sample vehicle collected by a positioning device may be obtained.

[0110] Step 405: It is determined whether the invalid time has exceeded 300 seconds.

[0111] Here, the invalid time is 300 seconds, which corresponds to the travel time for simulating the tunnel section being 300 seconds. If the invalid time exceeds 300 seconds, the next step 406 is executed.

[0112] Step 406: Set the file collected by the GNSS sensor as valid.

[0113] Note that setting the files collected by the GNSS sensor to valid after the invalid time exceeds 300 seconds corresponds to a scenario that simulates the vehicle traveling through a normal section after exiting a tunnel section.

[0114] Step 407: Read the file information.

[0115] Here, in response to a scenario in which the sample vehicle travels on a normal section, information in a file formed from the travel information of the sample vehicle collected by the GNSS sensor and other sensors on the vehicle, and the location information of the sample vehicle collected by the positioning equipment may be read out.

[0116] Step 408: Determine whether the end of the file has been reached.

[0117] If the end of the file has been reached, the process ends. If the end of the file has not been reached, the next step 409 is executed.

[0118] Step 409: It is determined whether the valid time has exceeded 200 seconds.

[0119] If the valid time exceeds 200 seconds, the next step 404 is executed, that is, the files collected by the GNSS sensor continue to be set as invalid. In this way, by periodically (i.e., alternately) setting the files of the GNSS sensor as invalid for 300 seconds and then as valid for 200 seconds in accordance with the time sequence, data of the sample vehicle in the tunnel section can be generated based on the data of the sample vehicle collected in the normal section.

[0120] Normally, when an autonomous vehicle enters a tunnel section, the driver may control the autonomous vehicle. Therefore, the most interesting period in the tunnel section is a predetermined period (e.g., 200 seconds) before entering the tunnel or the vehicle's driving information for a predetermined distance collected by the distance sensor. Based on this, sample vehicle data for the tunnel section can be generated by periodically disabling the GNSS sensor file for 300 seconds and then enabling it for 200 seconds.

[0121] It should be noted that the above times of 200 s and 300 s and speed of 20 km / h are specific examples given to facilitate understanding of the means of the embodiments of the present invention, and the above time or speed values ​​may be replaced with other numerical values, and the embodiments of the present invention are not limited to these.

[0122] Therefore, an embodiment of the present invention can simulate a tunnel section by periodically disabling and enabling the files collected by the GNSS sensor in a time sequence, thereby generating sample vehicle data corresponding to the tunnel section. That is, the section corresponding to the disabled part of the files collected by the GNSS sensor can be regarded as the tunnel section. This can obtain a training sample set corresponding to the tunnel section and ensure that the accuracy of the position information collected by the positioning device in the training sample set is sufficiently high.

[0123] Step 250: Train an accuracy estimation model based on the training sample set.

[0124] 3 may train the accuracy estimation model 307 based on the training sample set. For example, the model construction module 305 may input the training samples to the accuracy estimation model 307 to update parameters of the accuracy estimation model 307.

[0125] In some embodiments, different accuracy estimation models may be trained for different scenarios to obtain the best accuracy error estimation effect in each scenario. As an example, the following three scenarios may be included:

[0126] Scenario 1: Non-tunnel scenario + map scenario Scenario 2: Tunnel scenario + scenario with map Scenario 3: Non-tunnel scenario + no map scenario As an example, the above map may be a high-precision map, but the present invention is not limited thereto.

[0127] In the above scenario 1, the GNSS sensor is effective, and real-time positioning may be performed by combining the vehicle travel information collected by the sensor with map information.

[0128] In Scenario 1, the accuracy estimation model may specifically include a first accuracy estimation model. Third position information and second intermediate variables in a training sample corresponding to the first accuracy estimation model are determined based on the second travel information and map information (e.g., a high-precision map). The second travel information includes travel information of the sample vehicle collected by a GNSS sensor, and the second intermediate variables include error information between lane information determined based on the second travel information and lane information of the sample vehicle in map information.

[0129] As a specific example, in the training sample of the first accuracy estimation model, the second intermediate variables may include four types of intermediate variables in the above step 220, such as (1) intermediate variables obtained based on information collected by a visual sensor and a high-precision map, (2) intermediate variables obtained based on information collected by a GNSS sensor, a visual sensor, and a high-precision map, (3) covariance of the optimization algorithm, and (4) statistical values ​​of parameters collected within a certain period of time by a statistical sensor.

[0130] In the above-mentioned scenario 2, the GNSS sensor may be disabled, and real-time positioning may be performed by combining vehicle travel information collected by sensors other than the GNSS sensor with map information.

[0131] In Scenario 2, the accuracy estimation model may specifically be a second accuracy estimation model. Third position information and second intermediate variables in the training sample corresponding to the second accuracy estimation model are determined based on a valid portion of the second travel information and map information (e.g., a high-precision map). Travel information of the sample vehicle collected by the GNSS sensor in the second travel information is set to be partially invalid. The second intermediate variables include error information between lane information determined based on the second travel information and lane information of the sample vehicle in the map information.

[0132] Preferably, the travel information of the sample vehicle collected by the GNSS sensor in the second travel information is periodically set to invalid and valid in chronological order.

[0133] Specifically, the method for invalidating the travel information of the sample vehicle collected by the GNSS sensor may refer to the description of step 240 above, and the description thereof will be omitted here.

[0134] As a specific example, in the training sample of the second accuracy estimation model, the second intermediate variables may include four types of intermediate variables, such as (1) intermediate variables obtained based on information collected by a visual sensor and a high-precision map, (3) covariance of the optimization algorithm, and (4) statistical values ​​of parameters collected by a statistical sensor within a certain period of time (parameters collected by a GNSS sensor are not included) in step 220. For example, the statistical values ​​in (4) may be the average value and variance of the vehicle speed within 1 second, the average value and variance of the acceleration values ​​in the x-, y-, and z-axis directions of the accelerometer within 10 seconds, and the average value and variance of the angular velocity values ​​in the x-, y-, and z-axis directions of the gyro.

[0135] In the above scenario 3, the GNSS sensor is effective, but there is no map information, so real-time positioning cannot be performed by combining the vehicle driving information collected by the sensor with map information.

[0136] In Scenario 3, the accuracy estimation model may specifically be a third accuracy estimation model. Third position information and second intermediate variables in the training sample corresponding to the third accuracy estimation model are determined based on the second travel information. The second travel information includes travel information of the sample vehicle collected by a GNSS sensor.

[0137] As a specific example, in the training sample of the third accuracy estimation model, the second intermediate variables may include four types of intermediate variables, such as (3) the covariance of the optimization algorithm in step 220 above, and (4) the statistical values ​​of parameters collected by a statistical sensor within a certain period of time.

[0138] After preparing a training sample set for each of the above scenarios, a model may be constructed for each of the above scenarios, that is, an accuracy estimation model for each scenario may be trained based on the training sample set corresponding to each scene. Here, the accuracy estimation model may be a machine learning model, such as, but not limited to, a random forest model, an xgboost model, a deep neural network model, etc.

[0139] Below is an example of an algorithm configuration group when the xgboost model is used as the accuracy estimation model.

[0140] Sample size: 300,000 'booster': 'gbtree' 'objective': 'reg:gamma' or 'reg:squarederror' 'gamma': 0.1, 'max_depth': 6, 'lambda': 3, 'subsample': 0.7, 'colsample_bytree': 0.7, 'min_child_weight': 3, 'silent': 1, 'eta': 0.1 For example, the third location information, the fourth location information, and the second intermediate variable of the training sample in each scenario may be input into the accuracy estimation model of the corresponding scenario. The accuracy estimation model may obtain fifth location information of the sample vehicle and an accuracy error of the third location information based on the third location information and the second intermediate variable. Here, the accuracy error may be an error of the third location information relative to the fifth location information, but the present invention is not limited thereto.

[0141] Preferably, during the model training process, a first loss of the model may be determined based on the fifth location information and the fourth location information. Preferably, during the model training process, a second loss of the model may be determined based on an accuracy error of the third location information relative to the fifth location information and an accuracy error of the third location information relative to the fourth location information. Then, parameters of the accuracy estimation model may be updated based on at least one of the first loss and the second loss.

[0142] In some aspects, the accuracy error includes at least one of a lateral distance error, a longitudinal distance error, and a direction angle error.

[0143] Taking the error of the third position information relative to the fourth position information as an example, the distance between the third position information and the fourth position information decomposed into components perpendicular to the road is called a horizontal distance error, and the distance decomposed into components in the road direction is called a vertical distance error. As an example, the direction of the positioning device (e.g., a true value collecting device) may be the road direction. The road direction may also be called, but is not limited to, the driving direction.

[0144] If the fourth position information includes longitude lon0, latitude lat0, and vehicle direction angle heading0, and the third position information includes longitude lon, latitude lat, and vehicle direction angle heading, it may be determined that the accuracy error of the third position information relative to the fourth position information includes a lateral distance error disthorizontal, a longitudinal distance error distvetical, and a direction angle error.

[0145] Here, the direction angle error may be the difference between the vehicle direction angle heading0 and the vehicle direction angle heading, that is, heading0-heading.

[0146] The calculation process of the horizontal distance error disthorizontal and the vertical distance error distvetical is as follows.

[0147] First, calculate the distance dist between the two positions corresponding to the fourth position information and the third position information. Specifically, the following formula may be obtained based on the Haversine formula:

[0148]

number

[0149]

number

[0150]

number

[0151] Then, a direction angle "point_degree" between the two positions corresponding to the fourth position information and the third position information may be calculated. The direction angle "point_degree" is the angle by which the direction formed by the two positions corresponding to the fourth position information and the third position information deviates from the direction of the positioning device, and its value ranges from 0 to 360°. The calculation process of the direction angle "point_degree" is specifically as follows.

[0152]

number

[0153]

number

[0154]

number

[0155]

number

[0156] Finally, the horizontal distance error disthorizontal and the vertical distance error distvetical may be calculated based on the direction angle point_degree. The specific calculation process is as follows:

[0157]

number

[0158]

number

[0159]

number

[0160]

number

[0161]

number

[0162]

number

[0163]

number

[0164]

number

[0165]

number

[0166] There is also another scenario, namely, a tunnel scenario + no map scenario, in which even a high-precision sensor, such as an IMU sensor, cannot guarantee that the positioning result has sufficiently high accuracy, so in this scenario, it is set that positioning is unavailable and there is no need to estimate the positioning accuracy.

[0167] Therefore, in an embodiment of the present invention, based on the third position information of the sample vehicle determined using the driving information of the sample vehicle collected by the sensor, the second intermediate variables used in the process of determining the third position information and the fourth position information of the sample vehicle collected by the positioning device are determined, thereby realizing training of the accuracy estimation model and obtaining a trained accuracy estimation model. The accuracy estimation model according to the embodiment of the present invention can realize estimation of the accuracy error of the position information by combining the vehicle position information determined based on the vehicle driving information collected by the sensor and the intermediate variables used in the process of determining the position information.

[0168] After obtaining an accurate estimation model through training, we may proceed to the online estimation stage, which will be described below.

[0169] 5 is a schematic flowchart of a method 500 for estimating positioning accuracy according to an embodiment of the present invention. The method 500 may be performed by any electronic device having data processing capabilities. For example, the electronic device may be implemented as a server or a terminal device, or may be implemented as the computing module 109 in FIG. 1, but the present invention is not limited thereto.

[0170] In some aspects, the machine learning model may be included in (e.g., configured in) the electronic device and may be the accuracy estimation model described above. Continuing with reference to FIG. 3 , the vehicle information collection module 301, the real-time positioning module 302, the information statistics module 303, the information determination module 306, the accuracy estimation model 307, and the accuracy estimation module 308 may be used to estimate vehicle positioning accuracy. A method 500 for estimating positioning accuracy will now be described with reference to FIG. 3 .

[0171] As shown in FIG. 5, the method 500 includes steps 510-540.

[0172] Step 510: Obtain first driving information of the vehicle collected by a sensor.

[0173] For example, the vehicle information collection module 301 in FIG. 3 may acquire first driving information of the vehicle collected by a sensor. The sensor may collect the first driving information of the vehicle from the current period and the previous period, but is not limited thereto. After acquiring the first driving information, the vehicle information collection module 301 may transmit the first driving information to the real-time positioning module 302 and the information determination module 306. Preferably, the first driving information may be transmitted to the information statistics module 303.

[0174] In some aspects, the first driving information includes vehicle driving information obtained by at least one of an inertial measurement unit (IMU) sensor, a vehicle speed sensor, and a visual sensor.

[0175] Specifically, the first driving information collected by the sensor is the same as the second driving information collected by the sensor in step 210 of FIG. 2, and the above description may be referred to, and the description will be omitted here.

[0176] Step 520: Obtain first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information.

[0177] 3 may acquire first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information. After acquiring the first position information and the first intermediate variables, the real-time positioning module 302 may transmit the first position information and the first intermediate variables to the accuracy estimation model 307.

[0178] In some aspects, the first location information includes a longitude, a latitude, and a heading angle of the vehicle.

[0179] In some aspects, first position information and first intermediate variables may be obtained based on the first driving information and map information, where the first intermediate variables include error information between lane information determined based on the first driving information and lane information for the vehicle in the map information.

[0180] In some aspects, the first intermediate variable may include at least one of a covariance of an optimization algorithm used to determine the first location information and a statistical value of the first driving information within a first period of time.

[0181] 3 may compile statistical values ​​of parameters provided by the vehicle information collection module 301 within a predetermined period to obtain statistical values ​​of first driving information within a first period. After obtaining the statistical values, the information statistics module 303 may send the statistical values ​​to the real-time positioning module 302 or send the statistical variables to the accuracy estimation model 307.

[0182] Specifically, the first position information is the same as the third position information in step 220 in FIG. 2, and the first intermediate variable is the same as the second intermediate variable in step 220 in FIG. 2, and the description thereof will be omitted here.

[0183] Step 530: Determine a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, where the accuracy estimation model is obtained by training a machine learning model based on a training sample set, and the training samples in the training sample set include location information of a sample vehicle and intermediate variables used in the process of determining the location information.

[0184] Specifically, the training sample set may include third location information, fourth location information, and second intermediate variables used in the process of determining the third location information, where the third location information is determined based on second driving information of the sample vehicle collected by a sensor at a first time, and the fourth location information includes location information of the sample vehicle collected by a positioning device at the first time.

[0185] Specifically, the training process of the accuracy estimation model may refer to the above description in FIG. 2, and the description thereof will be omitted here.

[0186] In some aspects, the pre-trained accuracy estimation model includes a plurality of models each trained for a different scenario. In this case, a target accuracy estimation model may be determined in the pre-trained accuracy estimation model based on a first intermediate variable in the process of determining the first driving information and the first position information of the vehicle collected by the sensor. That is, a target accuracy estimation model applicable to the call may be determined according to a specific application scenario.

[0187] As an example, the information determination module 306 in FIG. 3 may determine an applicable accuracy estimation model for the call based on the first driving information and the first intermediate variable, i.e., based on the first driving information and whether there is currently available map information, such as a high-precision map.

[0188] As an example, if the first driving information includes vehicle driving information collected by a GNSS sensor and there is currently available map information (e.g., a high-precision map), the first accuracy estimation model may be determined as the target accuracy estimation model, i.e., it may be determined to call the first accuracy estimation model in the above scenario 1.

[0189] As another example, if the first driving information does not include vehicle driving information collected by a GNSS sensor and there is currently available map information (e.g., a high-precision map), the second accuracy estimation model may be determined as the target accuracy estimation model, i.e., it may be determined to call the second accuracy estimation model in scenario 2 above.

[0190] As another example, if the first driving information includes vehicle driving information collected by a GNSS sensor and there is no currently available map information (e.g., a high-precision map), the third accuracy estimation model may be determined as the target accuracy estimation model, i.e., it may be determined to call the third accuracy estimation model in scenario 3 above.

[0191] Specifically, for the first accuracy estimation model, the second accuracy estimation model, and the third accuracy estimation model, reference may be made to the description of step 250 in FIG. 2, and the description thereof will be omitted here.

[0192] Step 540: Input the first position information and the first intermediate variable into the target accuracy estimation model to obtain second position information of the vehicle and an accuracy error of the first position information relative to the second position information.

[0193] As an example, still referring to FIG. 3 , after the information determination module 306 determines the applicable target accuracy estimation model of the call based on the first driving information of the vehicle acquired by the sensor in the vehicle information collection module 301, while referring to whether there is currently available map information, such as a high-precision map, the real-time positioning module 302 and the information statistics module 303 may send the input-related features required for the accuracy estimation model to the target accuracy estimation model.

[0194] For example, when the first accuracy estimation model is invoked, the first intermediate variables may include (1) intermediate variables obtained based on information collected by a visual sensor and a high-precision map, (2) intermediate variables obtained based on information collected by a GNSS sensor, a visual sensor, and a high-precision map, (3) a covariance of the optimization algorithm, and (4) statistical values ​​of parameters collected by a statistical sensor within a certain period of time, which are used in the process of determining the first position information.

[0195] For example, when the second accuracy estimation model is invoked, the first intermediate variables may include (1) intermediate variables obtained based on information collected by a visual sensor and a high-precision map, (3) a covariance of an optimization algorithm, and (4) statistical values ​​of parameters collected by a statistical sensor within a certain period of time (excluding parameters collected by a GNSS sensor), which are used in the process of determining the first position information.

[0196] Also, for example, when the third accuracy estimation model is invoked, the first intermediate variables may include (3) the covariance of the optimization algorithm and (4) statistical values ​​of parameters collected by a statistical sensor within a certain period of time, which are used in the process of determining the first location information.

[0197] The first accuracy estimation model, the second accuracy estimation model, or the third accuracy estimation model may output predicted second position information of the vehicle and an accuracy error of the first position information relative to the second position when the relevant features are input.

[0198] Here, the second location information may be, but is not limited to, vehicle location information collected by a positioning device and predicted by an accuracy estimation model, or vehicle actual location information predicted by an accuracy estimation model. For example, the second location information includes the vehicle's longitude, latitude, and direction angle.

[0199] As an example, the target accuracy estimation model can predict the actual position information of the vehicle collected by the positioning equipment and estimate the accuracy error of the position information relative to the actual position information using the vehicle's positioning information determined based on the vehicle's driving information collected by the sensor and intermediate variables used in the process of determining the position information.

[0200] Therefore, an embodiment of the present invention can effectively evaluate the accuracy error of high-precision positioning by obtaining first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information, determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, inputting the first position information and the first intermediate variables into the target accuracy estimation model, and obtaining second position information of the vehicle and the accuracy error of the first position information relative to the second position information.

[0201] The method for estimating positioning accuracy according to the present invention significantly improved both the accuracy error CEP90 and the average value compared to conventional methods using optimized covariance. Here, CEP90 refers to the value at the 90th percentile when all accuracy errors are sorted in ascending order. For example, in a data set covering different road sections for approximately 2-3 hours each day over 20 days, the average accuracy error determined by the present invention was improved by an average of approximately 0.5 m compared to the covariance.

[0202] 6 is a diagram illustrating a specific example of a method 600 for estimating positioning accuracy according to an embodiment of the present invention. The method 600 may be performed by any electronic device having data processing capabilities. For example, the electronic device may be implemented as a server or a terminal device, or may be implemented as the computing module 109 in FIG. 1, but the present invention is not limited thereto.

[0203] It should be noted that while Figure 6 illustrates steps or operations of a method for estimating positioning accuracy, these steps or operations are merely examples, and embodiments of the present invention may perform other operations or variations of each operation in the figure. Also, the steps in Figure 6 may be performed in an order different from that shown in the figure, and not all of the operations in Figure 6 may be performed.

[0204] As shown in FIG. 6, the method 600 includes steps 601-610.

[0205] Step 601: Obtain first driving information of the vehicle.

[0206] Specifically, for step 601, the description of step 510 may be referred to, and the description thereof will be omitted here.

[0207] Step 602: Determine whether it is a tunnel.

[0208] Specifically, it may be determined whether the vehicle is in a tunnel section based on the first travel information in step 601. For example, if the first travel information includes vehicle travel information collected by a GNSS sensor, it may be determined that the vehicle is not in a tunnel section, and if the first travel information does not include vehicle travel information collected by a GNSS sensor, it may be determined that the vehicle is in a tunnel section.

[0209] For example, if the vehicle is in a tunnel section, the next step 603 is executed. If the vehicle is not in a tunnel section, the next step 605 is executed.

[0210] Step 603: Determine whether or not there is a high-precision map.

[0211] That is, in the tunnel scenario, it is further determined whether or not a high-precision map is available. If a high-precision map is available, the next step 604 is executed, and if a high-precision map is not available, the next step 610 is executed.

[0212] Step 604: Invoke the second accuracy estimation model.

[0213] Step 605: Determine whether or not there is a high-precision map.

[0214] That is, in a non-tunnel scenario, it is further determined whether there is a high-precision map. If there is a high-precision map, the next step 606 is executed, and if there is no high-precision map, the next step 607 is executed.

[0215] Step 606: Invoke the first accuracy estimation model.

[0216] Step 607: Invoke the third accuracy estimation model.

[0217] Specifically, for the first accuracy estimation model, the second accuracy estimation model, and the third accuracy estimation model, the explanations in FIGS. 2 and 5 may be referred to, and the explanations thereof will be omitted here.

[0218] Step 608: Perform parameter statistics.

[0219] Specifically, statistics may be performed on the driving parameters in the first driving information in step 601 to obtain statistical values ​​for each driving parameter.

[0220] Step 609: Perform real-time positioning.

[0221] Specifically, first location information of the vehicle may be determined based on the first driving information in step 601 and the statistical value obtained in step 608. The real-time positioning result (i.e., the first location information) and intermediate variables used in the process of determining the first location information may be input into corresponding accuracy estimation models. Specifically, the parameters input into different accuracy estimation models may refer to the description in FIG. 5, and the description thereof will be omitted here.

[0222] Step 610: Determine that a position fix is ​​unavailable.

[0223] That is, in the tunnel scenario and without high precision maps, it is determined that positioning is unavailable.

[0224] Step 611: Determine the accuracy error.

[0225] Specifically, the accuracy error of the real-time positioning result in different scenarios may be determined based on the results of calling the accuracy estimation model in different scenarios.

[0226] Therefore, an embodiment of the present invention can effectively evaluate the accuracy error of high-precision positioning by obtaining first position information of the vehicle and first intermediate variables used in the process of determining the first position information based on the first driving information, determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, inputting the first position information and the first intermediate variables into the target accuracy estimation model, and obtaining second position information of the vehicle and the accuracy error of the first position information relative to the second position information.

[0227] Although specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings, the present invention is not limited to the specific details of the above-described embodiments. Many simple modifications to the technical solutions of the present invention are possible within the scope of the technical concept of the present invention, and these simple modifications fall within the technical scope of the present invention. For example, the individual specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction, and to avoid unnecessary repetition, further description of various possible combinations will be omitted in this specification. For example, various embodiments of the present invention can be combined in any manner, and as long as they do not contradict the concept of the present invention, they will be considered to be similarly disclosed in this specification.

[0228] In various method embodiments of the present invention, the magnitude of the sequence numbers of the aforementioned processes does not mean the order of execution, and the execution order of the processes should be determined by their functions and inherent logic, and should not impose any limitations on the execution process of the embodiments of the present invention. In addition, these sequence numbers can be appropriately exchanged so that the described embodiments of the present invention can be implemented in an order other than that shown or described.

[0229] The above has described in detail an embodiment of the method of the present invention, and hereinafter, an embodiment of the apparatus of the present invention will be described in detail with reference to FIGS.

[0230] 7 is a schematic block diagram of a positioning accuracy estimation apparatus 700 according to an embodiment of the present invention. As shown in FIG. 7, the positioning accuracy estimation apparatus 700 may include an acquisition unit 710, a processing unit 720, a determination unit 730, and a target accuracy estimation model 740.

[0231] The acquisition unit 710 acquires first vehicle driving information from a sensor.

[0232] The processing unit 720 obtains, based on the first driving information, first position information of the vehicle and first intermediate variables used in the process of determining the first position information.

[0233] The determination unit 730 determines a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables. The accuracy estimation model is obtained by training a machine learning model based on a training sample set, and the training samples in the training sample set include location information of a sample vehicle and intermediate variables used in the process of determining the location information.

[0234] The target accuracy estimation model 740 receives the first position information and the first intermediate variable as input, and obtains second position information of the vehicle and an accuracy error of the first position information relative to the second position information.

[0235] In some aspects, the processing unit 720 acquires the first position information and the first intermediate variable based on the first driving information and map information, where the first intermediate variable includes error information between lane information determined based on the first driving information and lane information for the vehicle in the map information.

[0236] In some aspects, the first travel information includes travel information of the vehicle collected by a Global Navigation Satellite System (GNSS) sensor.

[0237] The determination unit 730 determines a first accuracy estimation model as the target accuracy estimation model based on the first driving information and the first intermediate variables, where the location information and the intermediate variables in the training sample set of the first accuracy estimation model are determined based on the driving information and map information of the sample vehicle collected by a GNSS sensor.

[0238] In some aspects, the determiner 730 determines a second accuracy estimation model as the target accuracy estimation model based on the first driving information and the first intermediate variables, where the location information and the intermediate variables in the training sample set corresponding to the second accuracy estimation model are determined based on a valid portion of the driving information of the sample vehicle collected by a GNSS sensor and the map information, and the driving information of the sample vehicle collected by the GNSS sensor is set to be partially invalid.

[0239] In some aspects, the sample vehicle driving information collected by the GNSS sensor is periodically set to invalid and valid in a time sequence.

[0240] In some aspects, the first travel information includes travel information of the vehicle collected by a GNSS sensor.

[0241] The determination unit 730 determines a third accuracy estimation model as the accuracy estimation model based on the first driving information and the first intermediate variables, where the position information and the intermediate variables in the training sample set corresponding to the third accuracy estimation model are determined based on the driving information of the sample vehicle collected by a GNSS sensor.

[0242] In some aspects, the first driving information includes driving information of the vehicle collected by at least one of an inertial measurement unit (IMU) sensor, a vehicle speed sensor, and a visual sensor.

[0243] In some aspects, the first intermediate variable includes at least one of a covariance of an optimization algorithm used to determine the first location information and a statistical value of the first driving information during a first period of time.

[0244] In some aspects, the first location information includes a first longitude, a first latitude, and a first vehicle direction angle of the vehicle, and the second location information includes a second longitude, a second latitude, and a second vehicle direction angle of the vehicle.

[0245] In some aspects, the accuracy error includes at least one of a lateral distance error, a longitudinal distance error, and a direction angle error.

[0246] It should be noted that the embodiment of the apparatus corresponds to the embodiment of the method, and reference may be made to the description of the embodiment of the method. To avoid repetition, the description thereof will be omitted here. Specifically, the apparatus 700 for estimating positioning accuracy according to this embodiment may correspond to an entity that executes the method 500 or 600 according to the embodiment of the present invention. The above-described operations and / or functions of each module in the apparatus 700 for estimating positioning accuracy are for realizing the corresponding flow of the method 500 or 600, respectively. Therefore, the description thereof will be omitted here for brevity.

[0247] 8 is a schematic block diagram of an apparatus 800 for training an accuracy estimation model according to an embodiment of the present invention. As shown in FIG. 8, the apparatus 800 for training an accuracy estimation model may include a first acquisition unit 810, a processing unit 820, a second acquisition unit 830, a determination unit 840, and a training unit 850.

[0248] The first acquisition unit 810 acquires second driving information of the sample vehicle at a first time from a sensor.

[0249] The processing unit 820 obtains, based on the second driving information, third location information of the sample vehicle and second intermediate variables used in the process of determining the third location information.

[0250] The second acquisition unit 830 acquires fourth position information of the sample vehicle at the first time from the positioning device.

[0251] The determining unit 840 determines a training sample set including the third position information, the fourth position information, and the second intermediate variable.

[0252] A training unit 850 trains the accuracy estimation model based on the training sample set.

[0253] In some aspects, the accuracy estimation model includes a first accuracy estimation model, and the second driving information includes driving information of the sample vehicle collected by a Global Navigation Satellite System (GNSS) sensor.

[0254] Here, the processing unit 820 acquires the third position information and the second intermediate variable based on the second driving information and map information, where the second intermediate variable includes error information between lane information determined based on the second driving information and lane information of the sample vehicle in the map information.

[0255] Here, the training unit 850 trains the first accuracy estimation model based on the training sample set.

[0256] In some aspects, the accuracy estimation model includes a second accuracy estimation model, and the second driving information includes driving information of the sample vehicle collected by a Global Navigation Satellite System (GNSS) sensor.

[0257] The processing unit 820 further invalidates part of the travel information of the sample vehicle collected by the GNSS sensor, and acquires the third position information and the second intermediate variable based on the valid part of the second travel information and map information, where the second intermediate variable includes error information between lane information determined based on the second travel information and lane information of the sample vehicle in the map information.

[0258] Here, a training unit trains the second accuracy estimation model based on the training sample set.

[0259] In some aspects, the accuracy estimation model includes a third accuracy estimation model, and the second driving information includes driving information of the sample vehicle collected by a Global Navigation Satellite System (GNSS) sensor.

[0260] Here, the training unit 850 trains the third accuracy estimation model based on the training sample set.

[0261] In some aspects, the second driving information includes driving information of the sample vehicle collected by at least one of an inertial measurement unit (IMU) sensor, a vehicle speed sensor, and a visual sensor.

[0262] In some aspects, the second intermediate variable includes at least one of a covariance of an optimization algorithm used to determine the third position information and a statistical value of the second driving information during a second period of time.

[0263] It should be noted that the device embodiment corresponds to the method embodiment, and reference may be made to the description of the method embodiment. To avoid redundancy, the description thereof will be omitted here. Specifically, the accuracy estimation model training device 800 according to this embodiment may correspond to an entity that executes the method 200 according to the embodiment of the present invention. The above-described operations and / or functions of each module in the accuracy estimation model training device 800 are for realizing the corresponding flow of the above-described method 200, and the description thereof will be omitted here for brevity.

[0264] The above describes the apparatus and system according to the embodiments of the present invention in terms of functional modules, with reference to the accompanying drawings. The functional modules may be implemented by instructions in hardware, software, or a combination of hardware and software modules. Specifically, steps of the method aspects according to the embodiments of the present invention may be performed by instructions in the form of integrated logic circuits and / or software in a hardware processor. The steps of the methods disclosed in connection with the embodiments of the present invention may be performed directly by a hardware decoding processor or by a combination of hardware and software modules in the decoding processor. Preferably, the software modules may be located in a storage medium established in the art, such as a random memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically rewritable programmable memory, or a register. Preferably, the storage medium is in a memory, and the processor reads information in the memory and executes the steps of the above method embodiments in combination with the hardware.

[0265] FIG. 9 is a schematic block diagram of an electronic device 1100 according to an embodiment of the present invention.

[0266] As shown in FIG. 9, the electronic device 1100 may include a memory 1110 and a processor 1120 .

[0267] The memory 1110 stores a computer program and transmits the program code to the processor 1120. In other words, the processor 1120 can call up and execute the computer program from the memory 1110 to implement the method according to the embodiment of the present invention.

[0268] For example, the processor 1120 may perform the steps of the method 200 described above according to instructions in a computer program.

[0269] In some embodiments of the present invention, processor 1120 may include, but is not limited to, a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc.

[0270] In some embodiments of the invention, memory 1110 may include, but is not limited to, volatile and / or non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM), acting as external cache memory. Various forms of RAM may be used, including, by way of example and not limitation, static random access memory (Static RAM (SRAM)), dynamic random access memory (DRAM)), synchronous dynamic random access memory (Synchronous DRAM (SDRAM)), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (Direct Rambus RAM (DR RAM)).

[0271] In some embodiments of the present invention, a computer program may be divided into one or more modules that are stored in memory 1110 and executed by processor 1120 to perform a method according to the present invention. The one or more modules may be a series of computer program instruction segments that describe the execution of the computer program in electronic device 1100 and may perform a particular function.

[0272] 9, the electronic device 1100 may further include a transceiver 1130 connectable to the processor 1120 or the memory 1110. The processor 1120 may control the transceiver 1130 to communicate with other devices, specifically, to transmit information or data to the other devices or to receive information or data transmitted by the other devices. The transceiver 1130 may include a transmitter and a receiver. The transceiver 1130 may further include an antenna, and the number of antennas may be one or more.

[0273] The components of the electronic device 1100 are connected by a bus system. Here, the bus system includes a power bus, a control bus, and a status signal bus in addition to a data bus.

[0274] According to one aspect of the present invention, there is provided a communications device including a memory having a computer program stored therein, and a processor, the processor calling and executing the computer program stored in the memory to cause an encoder to perform the method of the above-described method embodiment.

[0275] According to one aspect of the present invention, there is provided a computer storage medium having stored thereon a computer program which, when executed by a computer, causes the computer to perform the method of the above method embodiments. In other words, an embodiment of the present invention further provides a computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of the above method embodiments.

[0276] According to another aspect of the present invention, there is provided a computer program product or computer program comprising computer instructions stored on a computer readable storage medium, wherein a processor of a computing device reads the computer instructions from the computer readable storage medium and executes the computer instructions to cause the computing device to perform the method of the method embodiment described above.

[0277] In other words, when implemented using software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded into a computer and executed, the flow or function according to the embodiment of the present invention is generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored on a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (infrared, radio, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer, or a data storage device such as a server, data center, or the like that integrates one or more available media. The computer readable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), a semiconductor medium (e.g., a solid state disk (SSD)), or the like.

[0278] It should be noted that the computer-readable medium may be for related data such as user information in certain embodiments of the present invention. When the above embodiments of the present invention are applied to certain products or technologies, the computer-readable medium may need to obtain user permission or consent, and the collection, use, and processing of related data may need to comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0279] Those skilled in the art will understand that the exemplary modules and algorithm steps described in connection with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether the functions disclosed herein are performed in hardware or software depends on the specific application and design constraints of the technical solution. The functions described herein may be implemented in different ways by those skilled in the art for each specific application, but such implementations should not be considered outside the scope of this specification.

[0280] It should be noted that in some embodiments provided herein, the disclosed apparatus, device, and method may be realized in other ways. For example, the above-described apparatus embodiments are merely exemplary, and the division of modules is merely a division of logical functions. In actual implementation, additional division may occur. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not implemented. In addition, the illustrated or described couplings or direct couplings or communication connections between components may be indirect couplings or communication connections via some interfaces, devices, or modules, which may be electrical, mechanical, or other forms.

[0281] Modules illustrated as separate components may or may not be physically separated, and components shown as modules may or may not be physical modules, i.e., located in one location or distributed across multiple network elements. Separate or complete modules may be selected according to actual needs to achieve the objectives of the present embodiment. Separate physical entities may exist, or two or more modules may be integrated into one module.

[0282] The above are only specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any modifications or replacements that can be easily thought of by those skilled in the art within the technical scope disclosed in the present invention are within the scope of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for estimating positioning accuracy, executed by a positioning accuracy estimation device, comprising: acquiring first driving information of the vehicle from a sensor; obtaining first position information of the vehicle and first intermediate variables used in a process of determining the first position information based on the first driving information; determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, wherein the accuracy estimation model is obtained by training a machine learning model based on a training sample set, and training samples in the training sample set include location information of a sample vehicle and intermediate variables used in a process of determining the location information; and inputting the first position information and the first intermediate variable into the target accuracy estimation model, and obtaining second position information of the vehicle and an accuracy error of the first position information relative to the second position information.

2. The step of acquiring first position information of the vehicle and first intermediate variables used in a process of determining the first position information based on the first driving information includes:

2. The method of claim 1, further comprising: a step of acquiring the first position information and the first intermediate variable based on the first driving information and map information, wherein the first intermediate variable includes error information between lane information determined based on the first driving information and lane information of the vehicle in the map information.

3. the first driving information includes driving information of the vehicle collected by a Global Navigation Satellite System (GNSS) sensor; The step of determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variable includes:

3. The method of claim 2, further comprising: determining a first accuracy estimation model as the target accuracy estimation model based on the first driving information and the first intermediate variables, wherein the location information and the intermediate variables in the training sample set of the first accuracy estimation model are determined based on the driving information and map information of the sample vehicle collected by a GNSS sensor.

4. The step of determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variable includes:

3. The method of claim 2, further comprising: a step of determining a second accuracy estimation model as the target accuracy estimation model based on the first driving information and the first intermediate variables, wherein the location information and the intermediate variables in the training sample set corresponding to the second accuracy estimation model are determined based on a valid portion of the driving information of the sample vehicle collected by a GNSS sensor and the map information, and the driving information of the sample vehicle collected by the GNSS sensor is set partially invalid.

5. The method according to claim 4 , wherein the driving information of the sample vehicle collected by the GNSS sensor is periodically set to invalid and valid in a time sequence.

6. the first travel information includes travel information of the vehicle collected by a GNSS sensor; The step of determining a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variable includes:

2. The method of claim 1, further comprising: determining a third accuracy estimation model as the accuracy estimation model based on the first driving information and the first intermediate variables, wherein the position information and the intermediate variables in a training sample set corresponding to the third accuracy estimation model are determined based on driving information of the sample vehicle collected by a GNSS sensor.

7. The method of claim 1 , wherein the first driving information includes driving information of the vehicle collected by at least one of an inertial measurement unit (IMU) sensor, a vehicle speed sensor, and a visual sensor.

8. 2. The method of claim 1, wherein the first intermediate variables include at least one of a covariance of an optimization algorithm used to determine the first position information and a statistical value of the first driving information during a first time period.

9. 2. The method of claim 1, wherein the first location information includes a first longitude, a first latitude, and a first vehicle direction angle of the vehicle, and the second location information includes a second longitude, a second latitude, and a second vehicle direction angle of the vehicle.

10. The method of claim 1 , wherein the accuracy error includes at least one of a lateral distance error, a longitudinal distance error, and a direction angle error.

11. A method for training an accuracy estimation model, executed by an accuracy estimation model training device, comprising: acquiring second driving information of the sample vehicle at a first time from a sensor; obtaining third position information of the sample vehicle and second intermediate variables used in the process of determining the third position information based on the second driving information; acquiring fourth position information of the sample vehicle at the first time from a positioning device; determining a training sample set including the third location information, the fourth location information, and the second intermediate variable; training the accuracy estimation model based on the training sample set.

12. The accuracy estimation model includes a first accuracy estimation model, and the second driving information includes driving information of the sample vehicle collected by a Global Navigation Satellite System (GNSS) sensor; The step of acquiring third position information of the sample vehicle and second intermediate variables used in the process of determining the third position information based on the second driving information includes: a step of acquiring the third position information and the second intermediate variable based on the second travel information and map information, wherein the second intermediate variable includes error information between lane information determined based on the second travel information and lane information of the sample vehicle in the map information; training the accuracy estimation model based on the training sample set, The method of claim 11 , further comprising: training the first accuracy estimation model based on the training sample set.

13. the accuracy estimation model includes a second accuracy estimation model, and the second driving information includes driving information of the sample vehicle collected by a Global Navigation Satellite System (GNSS) sensor; The method further includes a step of partially invalidating the driving information of the sample vehicle collected by the GNSS sensor; The step of acquiring third position information of the sample vehicle and second intermediate variables used in the process of determining the third position information based on the second driving information includes: a step of acquiring the third position information and the second intermediate variable based on a valid portion of the second travel information and map information, wherein the second intermediate variable includes error information between lane information determined based on the second travel information and lane information of the sample vehicle in the map information; training the accuracy estimation model based on the training sample set, The method of claim 11 , further comprising: training the second accuracy estimation model based on the training sample set.

14. the accuracy estimation model includes a third accuracy estimation model, and the second driving information includes driving information of the sample vehicle collected by a Global Navigation Satellite System (GNSS) sensor; training the accuracy estimation model based on the training sample set, The method of claim 11 , further comprising: training the third accuracy estimation model based on the training sample set.

15. A positioning accuracy estimation device, an acquisition unit that acquires first driving information of the vehicle from a sensor; a processing unit that acquires first position information of the vehicle and first intermediate variables used in a process of determining the first position information based on the first driving information; a determination unit that determines a target accuracy estimation model in a pre-trained accuracy estimation model based on the first driving information and the first intermediate variables, wherein the accuracy estimation model is obtained by training a machine learning model based on a training sample set, and training samples in the training sample set include location information of a sample vehicle and intermediate variables used in a process of determining the location information; The target accuracy estimation model receives the first position information and the first intermediate variable as input, and obtains second position information of the vehicle and an accuracy error of the first position information relative to the second position information.

16. 1. A training device for an accuracy estimation model, comprising: a first acquisition unit that acquires second driving information of the sample vehicle at a first time from a sensor; a processing unit that acquires third position information of the sample vehicle and second intermediate variables used in a process of determining the third position information based on the second driving information; a second acquisition unit that acquires fourth position information of the sample vehicle at the first time from a positioning device; a determination unit that determines a training sample set including the third location information, the fourth location information, and the second intermediate variable; a training unit that trains the accuracy estimation model based on the training sample set.

17. 15. An electronic device comprising a processor and a memory having instructions stored thereon, the processor, when executing the instructions, performing a method according to any one of claims 1 to 14.

18. A computer program which, when executed by an electronic device, causes the electronic device to carry out a method according to any one of claims 1 to 14.

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